Predicting Performance Outcome With A Conversational Graph Convolutional Network For Small Group Interactions

Studying behaviors of members during small group interaction provides objective insights in improving the efficiency of the decision making process in our daily working life. By introducing the use of the graph structure in modeling the natural inter-member conversational ties during such an interaction, we aim to advance the state-of-art computational approach in predicting group performance scores. Specifically, we proposed a Conversational Graph Convolutional Network (CGCN) that utilizes conversation dynamic as the graph to aggregate group member's speech and lexical behaviors in predicting the group performance. Our result shows that Speech CGCN achieves the state-of-the-art performance at MSE 3.896 (0.323 Pearson correlation) outperform the current best method in ELEA dataset. Our model additionally reveals that an imbalance conversational graph structure is positively correlated to group performances.